Early detection of disease or malignant tissue can lead to a better prognosis. The development of non-invasive methods for detection and characterization of tumors and other anomalies has an extreme importance in current medicine. Dynamic, contrast-enhanced imaging provides an effective means of monitoring non-invasively and with high spatial and/or temporal resolutions the microvascular properties of tumors and tissues. The increased permeability of tumor vasculature gives rise to increased leakage of tracers including contrast agents, and enables characterization of enhancement patterns in the tissue. One method for characterization of tumor microvasculature is dynamic, contrast-enhanced (DCE) magnetic resonance imaging (MRI), or DCE-MRI. For DCE-MRI, multi-slice images are typically acquired before, during, and after the contrast agent infusion, resulting in the acquisition of a time sequence of image volumes, also referred to as a dynamic, contrast-enhanced image dataset.
Dynamic, contrast-enhanced image datasets can be post-processed using image analysis software to create supplemental data for interpretation by a radiologist. Such data can illustrate diagnostically important criterion that can not be evident from the original grayscale images. Examples of such supplemental data can include parametric maps, time-enhancement curve shape estimations, and/or multi-parametric analysis results. Some examples of image post-processing analysis techniques for creating such supplemental data can be seen in Breast MRI: Fundamentals and Technical Aspects, Hendrick, R. Edward, 2008, XVI, pp. 171-186 and US Published Patent Application No. 2009/0190806, entitled “METHOD FOR TRACKING OF CONTRAST ENHANCEMENT PATTERN FOR PHARMACOKINETIC AND PARAMETRIC ANALYSIS IN FAST-ENHANCING TISSUES USING HIGH-RESOLUTION MRI,” the contents of which are fully incorporated herein by way of useful background information.
Many image post-processing analysis techniques require identification of the arrival of contrast media administrated via the arterial/capillary system to the organ/tissue of interest. Contrast media arrival can vary widely depending on multiple factors, such as the speed and site of injection, location of organ/tissue in the body, patient blood flow patterns, etc. In many DCE image post-processing analyses, the time moment when contrast media concentration (and therefore, image signal intensity) achieves peak in major blood vessels or arteries close to the tissue/organ of interest signifies a key time point in a time array to be used for image post-processing analysis. In such cases, correct identification of contrast media arrival is required to generate correct diagnostic interpretation data.
In some semi-manual prior art solutions, a human user is required to visually inspect the dataset and provide either seeds or regions in an image from which contrast arrival can be detected. One such example is described, by way of background, in U.S. Pat. No. 7,233,687, entitled SYSTEM AND METHOD FOR IDENTIFYING OPTIMIZED BLOOD SIGNAL IN MEDICAL IMAGES TO ELIMINATE FLOW ARTIFACTS.
Requiring a clinician to perform a manual step on every DCE image dataset for interpretation is less than ideal, particularly in a clinical setting. However, attempts to provide automated solutions for detecting contrast arrival have experienced technical difficulties, due to undesired factors like imaging artifacts and/or patient motion, either of which can create the illusion within the image of enhancing arteries before or after the contrast media actually arrives. An example of this problem is visually illustrated in FIG. 1, which provides a plurality of DCE-MR images 100 particularly showing raw and subtraction images 110, 120 of a scanned prostate (and surrounding muscular tissues/vascular structures), and line graphs 130, 140 of time-signal intensity curves extracted from femoral arterial voxels near the prostate region. In this exemplary depiction, the images and line graphs are generated using the VividLook® post-processing software package available from iCAD, Inc. of Nashua, N.H. Notably, patient motion late in the imaging procedure has created the false illusion of signal enhancement in the femoral artery around the time that the 60th dynamic or temporal phase image volume is acquired. This is represented by the “false” peaks 150 and 160 in respective graphs 130 and 140. Thus the most reliable technique for addressing such unpredictable noise, artifacts and misleading factors has been the close monitoring of the procedure data by a human technician, who can apply educated judgment to more-accurately discern real from false information, and act accordingly. Clearly, this adds time and cost to procedures and can give rise to increased errors.